weka

I am trying to find how the C4.5 algorithm determines the threshold value for numeric attributes. I have researched and can not understand, in most places I've found this information: The training samples are first sorted on the values of the attribute Y being considered. There are only a finite number of these values, so let us denote them in sorted order as {v1,v2, …,vm}. Any threshold value lying between vi and vi+1 will have the same effect of dividing the cases into those whose value of the attribute Y lies in {v1, v2, …, vi} and those whose value is in {vi+1, vi+2, …, vm}. There are thus

What is the majority vote algorithm used in Weka. I tried to figure out its code but could not understand it. In Weka you can select multiple classifiers to be used in Weka.classifiers.meta.vote . If you select Majority Voting as combinationRule (which only works with nominal classes), then each of these classifiers will predict a nominal class label for a test sample. The label which was predicted the most will then be selected as output of the vote classifier. For example. You select the following classifiers to be used: trees.J48 , bayes.NaiveBayes and functions.LibSVM to predict the

I have a JSON file and want to open the data in weka, but when I do, I get the following error: Looking around on the mailing list , there are a few questions about JSON, but TL;DR except that I noticed talk of JSON in the "format weka expects". Of course, there was no mention of what that format is. About to take a dive in the source, but I hope SO users can help before I spend too much time on this. To gain an understanding about the format of the JSON object and its relationship to ARFF. The steps were surprisingly simple. Use the GUI tool to do the following: Select the Explorer Option

I have to perform a k-means clustering on a really huge matrix (about 300.000x100.000 values which is more than 100Gb). I want to know if I can use R software to perform this or weka. My computer is a multiprocessor with 8Gb of ram and hundreds Gb of free space. I have enough space for calculations but loading such a matrix seems to be a problem with R (I don't think that using the bigmemory package would help me and big matrix use automatically all my RAM then my swap file if not enough space). So my question is : what software should I use (eventually in association with some other packages

I'm trying to increase the heap size in java for weka which keeps crashing. I used the suggested line: > java -Xmx500m -classpath but I get the following error: -classpath requires class path specification I'm not sure what this means. Any suggestions? What I found was the actual issue was in the file 'RunWeka.ini' in '\Program Files (x86)\Weka-3-6' . I opened it with notepad and in the middle of the file there is a line 'maxheap = 512m' . I changed the line to read 'maxheap=2000m' , saved the file and reloaded weka and this fixed my problems. I'm not sure if this is the correct way to do it

I am fairly new to Weka and even more new to Weka on the command line. I find documentation is poor and I am struggling to figure out a few things to do. For example, want to take two .arff files, one for training, one for testing and get an output of predictions for the missing labels in the test data. How can I do this? I have this code as a starting block java -classpath weka.jar weka.classifiers.meta.FilteredClassifier -t "training_file_with_missing_values.arff" -T "test_file_with_missing_values.arff" -F weka.filters.unsupervised.attribute.ReplaceMissingValues -- -c last -W weka